A genetic algorithm inspired task scheduling in cloud computing

Agarwal, Mohit; Srivastava, Gur Mauj Saran

doi:10.1109/ccaa.2016.7813746

Cited by 44 publications

(18 citation statements)

References 11 publications

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“…It outperforms an adaptive GA and some other GAs in terms of convergence speed, feasibility and effectiveness. Agarwal and Srivastava [58] proposed a GA for task scheduling in CC, which distributes the loads among VMs effectively to optimize the overall response time. Experimental results show that the proposed algorithm outperforms some existing techniques such as greedy-based techniques and First Come First Serve in terms of overall response time.…”

Section: A Eas In CC and Ecmentioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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Section: A Eas In CC and Ecmentioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

View full text Add to dashboard Cite

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“…In [ 36 ], a novel architecture is proposed, in addition to a task scheduling algorithm based on a dynamic scheduling queue algorithm and particle swarm optimization algorithm, and this algorithm fully considers the dynamic characteristic of the cloud computing environment, with experimental results that show the architecture can effectively achieve good performance, load balancing and improvements in resource utilization. In [ 37 ], an improved genetic algorithm for the task scheduling strategy was proposed. Moreover, in some literature, combinations of multiple single algorithms to form a hybrid algorithm for solving the task scheduling problem are proposed.…”

Section: Related Workmentioning

confidence: 99%

Task Scheduling Based on a Hybrid Heuristic Algorithm for Smart Production Line with Fog Computing

Wang

2019

Sensors

111

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Fog computing provides computation, storage and network services for smart manufacturing. However, in a smart factory, the task requests, terminal devices and fog nodes have very strong heterogeneity, such as the different task characteristics of terminal equipment: fault detection tasks have high real-time demands; production scheduling tasks require a large amount of calculation; inventory management tasks require a vast amount of storage space, and so on. In addition, the fog nodes have different processing abilities, such that strong fog nodes with considerable computing resources can help terminal equipment to complete the complex task processing, such as manufacturing inspection, fault detection, state analysis of devices, and so on. In this setting, a new problem has appeared, that is, determining how to perform task scheduling among the different fog nodes to minimize the delay and energy consumption as well as improve the smart manufacturing performance metrics, such as production efficiency, product quality and equipment utilization rate. Therefore, this paper studies the task scheduling strategy in the fog computing scenario. A task scheduling strategy based on a hybrid heuristic (HH) algorithm is proposed that mainly solves the problem of terminal devices with limited computing resources and high energy consumption and makes the scheme feasible for real-time and efficient processing tasks of terminal devices. Finally, the experimental results show that the proposed strategy achieves superior performance compared to other strategies.

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“…Contrary to heuristic-based, the meta-heuristic methods search the solution space in a direct manner and produce efficient results on the broad domain problems, but these methods have high time complexity. Meta-heuristic algorithms are also called guided-random search-based methods [18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: R Re El La At Te Ed D W Wo or Rk Kmentioning

confidence: 99%

A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

Nasr

El‐Bahnasawy

Attiya

et al. 2019

Menoufia Journal of Electronic Engineering Research

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A A P Pr ro op po os se ed d M Me et ta a--H He eu ur ri is st ti ic c A Ap pp pr ro oa ac ch h f fo or r C Cl lo ou ud dl le et ts s S Sc ch he ed du ul li in ng g i in n C Cl lo ou ud d C Co om mp pu ut ti in ng g E En nv vi ir ro on nm me en nt t A Ab bs st tr ra ac ct tThis paper presents a new hybrid approach, called ACOSA, for cloudlets scheduling to enhance the scheduler behavior in Cloud computing (CC) environment and to overcome the results oscillation problem of the existing meta-heuristic scheduling algorithms. The proposed approach combines both the Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithm to improve both quality of solutions and time complexity of the scheduling algorithm. The proposed approach is evaluated by using the well-known CloudSim, and the results are compared with the ant colony and simulated annealing separately in terms of schedule length, load balancing, and time complexity. It decreases the schedule length by 29.75% with SA and 12.25% with ACO. The ACOSA provides higher load balancing degree. It improves the balancing degree ratio by 36.36% than SA and 12.13% than ACO algorithms.

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A genetic algorithm inspired task scheduling in cloud computing

Cited by 44 publications

References 11 publications

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Task Scheduling Based on a Hybrid Heuristic Algorithm for Smart Production Line with Fog Computing

A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

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